Zero-Trust enforcement for enterprise AI.
DeepInspect authorizes, redacts, or blocks every AI request inline, then writes a cryptographically signed forensic record for regulatory defense.
Four surfaces of the AI governance problem, one control plane.
The gateway sits in the request path with sub-50ms overhead, evaluates policy, and writes an evidence-grade record before the response is released.
Intelligent Security
Identity- and data-aware enforcement. Inline. Fail-closed.
Verifiable Governance
HMAC-signed forensic record for every decision.
Cost & Routing
Tier-based model routing, failover, per-token attribution.
Agent & MCP Governance
Per-tool policy and signed agent traces across every hop.
Who Owns AI Governance Liability in an Enterprise?
AI governance is a responsibility boundary. Regulators and boards want enforcement records, not dashboards.
The boundary falls on the enterprise rather than the model provider. Provider terms of service place contractual responsibility for how a model is used inside a customer environment on the customer. A regulatory inquiry about a specific AI decision reaches the CISO and the compliance officer, and the durable answer is a runtime record of how each AI request was handled together with the policy version in effect at the time.
Enterprises own:
Each obligation maps to an operational record only the enterprise can produce. Regulatory exposure resolves when the enterprise shows the specific decisions the system made and the policy versions in effect. Audit outcomes improve when the auditor reads records that each carry a per-record cryptographic signature verifiable on its own. Breach narratives hold together when every AI interaction in the sequence carries its own integrity proof. Board accountability follows the pattern of financial controls, signed attestations rather than best-effort summaries.
Governance requires reconstruction of who accessed what data and why a decision was allowed.
The risk lives in ungoverned AI usage inside your enterprise.
Point your AI client at the gateway. Nothing else changes.
Enforcement activates gradually from observe mode to enforce mode, per application and per data class. Application code stays intact.
Product
Inline enforcement between callers and models.
Identity-aware policy, deterministic evaluation, fail-closed by default. Sub-50ms overhead in the request path.
Forensics
Signed record for every AI decision.
Reconstruct any interaction. Verify integrity per record.
Architecture
Deterministic policy engine, default-deny posture.
Deploys inline. Payload-agnostic. Provider-neutral.
< 50ms
Gateway overhead
100%
Decisions signed
6+
Providers supported
HMAC
Per-record integrity
Recent thinking.
All posts →July 23, 2026
Semantic Caching at the LLM Gateway: What It Saves and the Four Ways It Leaks
Semantic caching answers a new prompt with a stored response when the embeddings are close enough. It cuts cost and latency, and it introduces a shared read path across whatever tenants and identities share the cache namespace. This covers the architecture, the similarity threshold problem, four concrete leak paths including cross-tenant hits and stale policy decisions, and the partitioning rules that make a cache safe to run.
July 23, 2026
Pydantic AI Behind a Gateway: Routing, Identity, and What the Type System Does Not Cover
Pydantic AI gives an agent a typed output contract, typed dependencies, and validated tool signatures, which removes a real class of parsing and argument bugs. Schema validation runs after the model call and inside the process, so it covers structure rather than authorization, egress, or evidence. This walks what the type system enforces, the four gaps that remain, and how to route a Pydantic AI agent through a gateway without changing agent code.
July 23, 2026
LLM Tool Integration Security: The Trust Boundary Between a Model and the Functions It Calls
Giving a model tools converts text generation into action. The model emits a structured call, your code executes it, and the result goes back into the context window. Three trust boundaries sit inside that loop: the tool description the model reads, the arguments it emits, and the output that returns as trusted context. This walks the failure at each boundary, the validation each one needs, and where credential scoping does work no HTTP control can substitute for.